Background & Track Selection

How do two completely different musical moods take shape in data? This dashboard presents a comparative analysis of two AI-generated tracks I created using Stableaudio AI. One track explores ambient minimalism, while the other is a high-energy breakbeat.

Track 1: Meditative Ambient Soundscape
- Tags: Ambient, Post-Rock, Minimalist, Reverb, 60 BPM

Track 2: Energetic Breakbeat Rave
- Tags: Breakbeat, Acid Breaks, Chaotic, 135 BPM

Learning Goals

As an Information Science student, both the world of computational music analysis and music production were fairly new territories to me at the start of this project. While I was familiar and comfortable with working with data, exploring music through features like tempo, valence, or timbre was entirely new to me. Prior to this project I had only limited knowledge about music theory, although I have always had a casual interest in music at its broadest. As for the computational aspects I did have a bit of RStudio knowledge, though I was used to working with different types of data and visualizations than those explored in this course, so even this was novel for me.

Starting off, I experimented with multiple AI music generators, but many of them felt too rigid or unpredictable. Eventually, I settled on Stableaudio AI for its detailed prompt control and more satisfying sonic results. My goal was to create two starkly contrasting tracks: one calm and ambient, the other chaotic and energetic, so that I could explore the musical and data-level differences more clearly across various analytical features.

If I had more time or production experience, I would have loved to compose or produce my own music from scratch. However, given the time constraints and learning curve using AI allowed me to focus more on the analytical side of this project so that the AI tracks could be used as case studies to compare both with each other and with the class corpus.

Ultimately, my goal during this project was not just the generation of music, but rather to develop a deeper understanding of how music by exploring how differences between two songs become visible through data.

Listen to Track 1 (Ambient)

Listen to Track 2 (Breakbeat)

Scatterplot - Danceability vs. Tempo

To begin my analysis, I wanted a straightforward visualisation that would let me place my own tracks within the context of the class corpus. I chose a scatterplot because it clearly separates each data point, which helped me to quickly identify how my tracks compare in terms of danceability and tempo, as these two features often shape the overall feel of a track.

This interactive scatterplot maps Danceability against Tempo of the tracks, which helps to position my two AI-generated tracks within the entire class corpus.

NOTE Interestingly enough, this scatterplot has changed quite a bit from the first time I had generated it: before this, my ambient track (red) was positioned as both slow and undanceable, while the breakbeat track was slightly higher in both tempo and danceability. For some reason I cannot figure out, this changed sometime during the last few weeks. Perhaps there was an adjustment to the class corpus features, and it impacted the estimated tempo of those two tracks. It now states that my ambient tracks sits at over 150 BPM, while I generated it to be much lower than that, and certainly not faster than the breakbeat track. So, my analysis below still reflects this interpretation, as this was actually rooted in reality.

There appears to be no set correlation between the danceability and tempo of the tracks. However, an interesting pattern emerges: there are two clusters, one with low danceability, and another with high danceability, while the tempo does not differ much.

Regarding my own tracks:

• The calm ambient track has an average tempo and low danceability.
• The breakbeat song has an average tempo but high danceability.

One particularly surprising observation is how the AI interpreted the second song’s tempo. While I set it to 135 BPM, it was classified as 93 BPM. This suggests that the AI might have emphasized a different rhythmic structure or half-time feel in its classification.

EDIT As stated above, here is another case of the estimated tempo shifting, but this time for the breakbeat track. Thus, tempo does not appear to be a very consistently evaluated variable. Perhaps each method counts their notes differently.

Overall, the plot allowed me to see individual track positions and emerging clusters. As visible here, there’s a group of tracks with low danceability and another with high danceability, while tempo remains relatively consistent across both clusters. This helped me gain more perspective into where my own tracks ‘live’ compared to the others, which created a solid mental foundation for further explorations.

Chromagrams for Class Corpus & Own Tracks

Before diving into the analysis, it’s helpful to briefly explain what a chromagram is. A chromagram is a visual representation of harmonic content in music. It maps audio to the 12 pitch classes (C, C#, D, …, B), showing how energy is distributed across pitches over time, regardless of octave. This is especially useful when analyzing chord progressions, tonality, or harmonic density.

1. Technical Issues

Creating these chromagrams proved to be a bit of a journey for me. I initially struggled because I missed the session where we were introduced to the compmus.R helper file. As I did not know it existed, I tried calculating chromagrams (and later, self-similarity matrices) using external packages like tuneR and seewave.

Eventually, I did manage to get my plots working, though it took a lot of trial and error. Even after discovering the compmus.R file, I ran into some persistent bugs, possibly due to updates to the helper functions or conflicts in how I had structured my analysis. For now, I’ve kept my original chromagram approach intact, since it renders reliably in R.

One thing I did notice was how the resulting spectrograms could benefit from better log-frequency mapping. Without it, the lower frequencies, especially in my ambient track, appear faint or almost non-existent. This is a fair reflection though, as the track is meant to be minimal and quiet.

2. Initial Attempt (Failed)

Below are screenshots from my first attempt at generating chromagrams using waveform-based spectrograms via tuneR and seewave.
While they visually resembled spectrograms, they did not accurately reflect pitch-class content and failed to render properly within the RMarkdown dashboard.

This took quite a lot of time to display this graphic, you may set 'fastdisp=TRUE' for a faster, but less accurate, display

3. Chromagram 1.0 Results

Track 1 — Ambient

Energy is concentrated in the lower frequency range (below ~5 kHz)
The overall amplitude is low, reflecting the sparse, ambient atmosphere
The structure is fluid and minimal, with little rhythmic emphasis

Track 2 — Breakbeat

Frequency content spans a much wider range, reaching above 10 kHz
Brighter colors indicate greater amplitude and more harmonic variation
Suggests a more rhythmic, melodic, and dynamic structure, consistent with breakbeat styles

So, although the chromagram for the ambient track appears faint and minimal, this is true to the source material which was designed to be slow, textured, and atmospheric. In contrast, the breakbeat track is vibrant and information-dense, with sharper contrasts and more visible harmonic shifts.

4. Chromagram 2.0 Results

Track 1 — Evan-l-1.wav (Ambient):

There is a clear strong harmonic emphasis on F#, D#, and C#, with F# especially dominant.F# remains smoothly present and stable throughout most of the likely making it the tonic, thus track 1 can be described as a tonally consistent piece with little modulation. This aligns with the overall genre conventions of ambient tracks, where strong tonal consistenty with slow shifts play an important role in creating the wanted atmosphere.

Track 2 — Evan-l-2.wav (Breakbeat):

The harmonic content appears much more scattered over multiple pitch classes, with some light bursts of color in D, D#, and G. The texture appears relatively unstable, which aligns with the more complex and evolving harmonic structure the song has. This also reflects the genre conventions of breakbeat, which usually have rapid tonal shifts.

Conclusively, these two chromagrams helped me further confirm the contrast I was aiming for: one track being repetitive and stable, while the other is chaotic and rhythmically all over the place.

SSM Visuals

Within the context of my musical exploration these SSMs helped me explore how much variety or repetition exists in my tracks—and whether those patterns aligned with what I intended in the composition prompts. As with the chromagrams, I initially used external packages to generate my self-similarity matrices (tuneR, seewave, and melfcc() from phonTools). Luckily, the output remains very clear and useful, even if slightly stylistically different from the class standard.

SSM Interpretation

• Evan-l-1.wav - Ambient
  • The chroma SSM is fairly uniform, with only mild internal variation. This fits the track’s ambient purpose: minimal harmonic shifts and slow movement.
  • The timbre SSM is smooth and almost static, reflecting the consistent use of textures and sparse instrumentation.
• Evan-l-2.wav - Breakbeat
  • The chroma SSM features a checkerboard-like structure, which suggests the presence alternating harmonic material or call-and-response phrasing.This is apparently typical of fast-paced breakbeat.
  • The timbre SSM shows greater diversity and shifting textures, likely due to drum fills, chopped vocal samples, and synthesized layers that I requested in my prompt.

Despite being AI-generated, both tracks exhibit recognizable structural patterns that align with their intended genres. The ambient track relies on repetition and slow shifts, while the breakbeat track emphasizes variety and rhythmic segmentation. These SSMs were one of the clearest visual confirmations of that difference.

I used the ‘viridis’ colour scale, in which darker colours imply a stronger presence, while lighter colours indicate absence.

Tempo Histogram for Class Corpus

Before analyzing my own tracks’ tempo structures in detail, I first wanted to understand how tempo is distributed across the entire class corpus. My goal was to see where my own AI-generated tracks might fit within the broader spectrum of musical pacing in the dataset.

To do this I chose to visualize the tempo of each track using a histogram. A bar plot is one of the most straightforward ways to represent frequency distributions, and in this context, it clearly displays which tempos are most common in the class corpus. I also used Plotly to make the plot interactive, so individual tempo bins can be hovered over to reveal exact values.

From the histogram, we can see that the most frequent tempo (mode) in the class corpus is 85 BPM, with a count of 16 tracks. This relatively slow pace is commonly associated with genres like hip-hop, downtempo electronic, or ballads—genres that often center around groove or mood rather than rapid movement. It also aligns roughly with the human resting heart rate, which may contribute to its perceived ‘naturalness’.

The tempo distribution also shows smaller peaks in higher ranges, this could hint at clusters of tracks with faster or more danceable tempos like those found in house, pop, or techno.

Tempogram – Track 1 (Ambient)

After establishing the previous benchmark, I visualized the tempograms of my two AI-generated songs. These tempograms are generated from the JSON feature files and show how rhythmic intensity evolves over the duration of each track.

Tempogram – Track 2 (Breakbeat)

Tempogram – Track 2 (Breakbeat)

• Track 1 - Ambient
  • The tempogram is fairly uniform and flat for the first half. This is consistent with how smooth and uniform the track is.
  • Around the 60-second mark, the regularity begins to break down, and we observe some fragmentation and drift in the tempo bands. This showcases the more layered elements that are audible in this track, such as the addition of audio tape samples.
• Track 2 - Breakbeat
  • This track displays clearly defined, horizontal tempo bands, which indicates a steady and repetitive beat.
  • The multiple stacked lines reflect regular subdivisions (ex: half-time, double-time) which are typical in breakbeat and rave music.
  • The activity is highly dynamic and colorful, which reflects the energetic and intense piece of music.
  • There’s a minor disturbance just past the halfway point—likely a bridge or breakdown before returning to the core rhythmic pattern.

Comparing these tempograms visually strengthens the contrast in structure between my two tracks. The ambient piece is loosely timed, smooth, and much more textural, while the breakbeat track is rigid, fast-paced, and driven by its beat. This rhythmic contrast further supports what was established in earlier keygrams and SSMs, as each track is clearly distinct in both feel and temporal organization.

Dendrogram Plot

Mood-Based Dendrogram Plot

In this mood-based dendrogram, tracks that cluster lower tend to be more similar in emotional character. In this case, that often means relaxed, calm, or lo-fi — including my ambient track. My breakbeat track, on the other hand, clusters higher up, which indicates that it’s more distinct and intense in mood and energy.

To conclude, this dendrograms pattern aligns reasonably well with my own two tracks. My ambient piece is grouped low in the dendrogram, nestled among similarly relaxed tracks. Meanwhile, the breakbeat track is positioned much higher, reflecting its chaotic energy and rhythmic aggression. Seeing this contrast play out in the structure of the dendrogram was especially satisfying as it served as a visualization for something that is often only describable as a ‘vibe’ or ‘energy’, now being measurably present in the data.

Feature Summary Table

Reflections on the Process

Coming into this project without a strong background in either computational analysis or musicology, I wasn’t entirely sure what to expect. My experience with R was minimal and limited to a single data science course two years ago, and my musical understanding was more intuitive than technical. But working through this analysis gave me a surprisingly rich perspective on how musical qualities manifest in data.

What stood out to me the most was seeing the contrast between my two AI-generated tracks unfold across each visualization. Because the tracks were intentionally so different (one ambient and meditative, the other energetic and chaotic) the distinctions were clear, which made it much easier to understand what each method was actually showing.

Whether looking at chromagrams, self-similarity matrices, or keygrams, I could begin to interpret the visuals in a way that matched what I had heard intuitively. Even moments like sensing a key shift without knowing the actual notes, only to see that shift appear in the analysis, helped recontextualize how I listen to music altogether. It felt like learning a new language which allowed me to describe and understand music in ways I couldn’t before.

Takeaways for Future CompMus Beginners

This kind of process could be especially valuable for beginners in musicological analysis, particularly those exploring electronic instrumental music. The contrast between tracks, when made visual, becomes a powerful learning tool. Starting with minimal R knowledge, I found that the code gradually became more intuitive. Flexdashboard, ggplot2, and the compmus helper functions were all far more accessible than I expected once I got into the rhythm of things. My own analysis could serve as a way to better understand these features for other ‘compmus beginners’ as well. Being able to see harmony, rhythm, structure, and energy not only supports deeper listening, but also helps bridge the gap between abstract sound and analytical clarity.